Data entry from series of images of a patterned document

ABSTRACT

The present disclosures provide methods of optical character recognition for a patterned document having one static element and one information field. Systems and methods are disclosed to identify in each of a current and a previous image of a series of images of an original document overlapping with each other, a corresponding plurality of base points, wherein each base point is associated with one textural artifact in each of the current image and the previous image using an OCR text of the current image; identify parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associate a part of the OCR text with a cluster of a plurality of clusters of symbol sequences; identify a median string representing the cluster of symbol sequences; and produce a resulting OCR text representing at least a portion of the original document.

CROSS REFERENCE TO RELATED APPLICATIONS

The present Application claims the benefit of priority under 35 USC 119to Russian Patent Application No. 2016125289, filed Jun. 24, 2016,Russian Patent Application No. 2016118635, filed May 13, 2016, andRussian Patent Application No. 2016118633, filed May 13, 2016. Thepresent Application is also a Continuation of U.S. patent applicationSer. No. 15/195,603, filed on Jun. 28, 2016, a Continuation-In-Part ofU.S. patent application Ser. No. 15/168,548, and U.S. patent applicationSer. No. 15/168,525, both filed on May 31, 2016; disclosure of priorityapplications are incorporated herein by reference in their entirety forall purposes.

FIELD

The present disclosure relates generally to computer systems andmethods, and, more specifically, to systems and methods for opticalcharacter recognition (OCR), including OCR systems and methods forextracting information from structured (fixed) documents.

BACKGROUND

Optical character recognition (OCR) is a computer-implemented conversionof text images (including typed, handwritten, or printed text) intocomputer-encoded electronic documents.

SUMMARY

One embodiment is a method, comprising: (a) receiving, by a processingdevice of a computer system, a current image of a series of images of acopy of a patterned document, wherein the patterned document has atleast one static element and at least one information field; (b)performing optical character recognition (OCR) of the current image toproduce an OCR text and corresponding coordinates for each symbol of theOCR text; (c) identifying parameters of a coordinate transformation totransform coordinates of the current image into coordinates of atemplate, wherein the template contains i) a text and coordinates forthe at least one static element of the patterned document and ii)coordinates for the at least one information field of the patterneddocument; (d) identifying in the OCR text for the current image in thecoordinate system of the template a text fragment that corresponds to aninformation field of the at least one information field; (e) associatingin the coordinate system of the template the text fragment thatcorresponds to the information field with one or more clusters of symbolsequences, wherein the text fragment is produced by processing thecurrent image and wherein the symbol sequences are produced byprocessing one or more previously received images of the series ofimages; (f) producing, for each cluster of the one or more clusters, amedian string representing the cluster of symbol sequences for theinformation field; and (g) producing, using the median string of each ofthe one or more clusters, a resulting OCR text that represents anoriginal text of the information field of the copy of the patterneddocument.

Another embodiment is a method, comprising: (a) receiving, by aprocessing device of a computer system, a current image of a series ofimages of a copy of a patterned document, wherein the current image atleast partially overlaps with a previous image of the series of imagesand the patterned document has at least one static element and at leastone information field; (b) performing optical symbol recognition (OCR)of the current image to produce an OCR text and correspondingcoordinates for each symbol of the OCR text; (c) identifying parametersof a coordinate transformation to transform coordinates of the previousimage into coordinates of the current image; (d) associating at leastpart of the OCR text with one or more clusters of symbol sequences,wherein the OCR text is produced by processing the current image andwherein the symbol sequences are produced by processing one or morepreviously received images of the series of images; (e) identifyingparameters of a coordinate transformation to transform the coordinatesof the current image of the series of images, into coordinates of atemplate of the patterned document, wherein the template contains i) atext and coordinates for the at least one static element of thepatterned document and ii) coordinates for the at least one informationfield of the patterned document; (f) identifying in the coordinatesystem of the template, one or more clusters of symbol sequences thatcorresponds to an information field of the at least one informationfield; (g) producing, for each of the one or more clusters thatcorresponds to the information field, a median string representing thecluster of symbol sequences for the information field; and (h)producing, using the median string of each of the one or more clusters,a resulting OCR text that represents an original text of the informationfield of the copy of the patterned document.

Another embodiment is a system, comprising: A) a memory configured tostore a template of a patterned document, which comprises at least onestatic element and at least one information field, wherein the templatecontains i) a text and coordinates for the at least one static elementof the patterned document and ii) coordinates for the at least oneinformation field of the patterned document; B) a processing device,coupled to the memory, the processing devise configured to: (a) receivea current image of a series of images of a copy of the patterneddocument; (b) perform optical character recognition (OCR) of the currentimage to produce an OCR text and corresponding coordinates for eachsymbol of the OCR text; (c) identify parameters of a coordinatetransformation to transform coordinates of the current image intocoordinates of the template; (d) identify in the OCR text for thecurrent image in the coordinate system of the template a text fragmentthat corresponds to an information field of the at least one informationfield; (e) associate in the coordinate system of the template the textfragment that corresponds to the information field with one or moreclusters of symbol sequences, wherein the text fragment is produced byprocessing the current image and wherein the symbol sequences areproduced by processing one or more previously received images of theseries of images; (f) produce, for each cluster of the one or moreclusters, a median string representing the cluster of symbol sequencesfor the information field; and (g) produce, using the median string ofeach of the one or more clusters, a resulting OCR text that representsan original text of the information field of the copy of the patterneddocument.

Another embodiment is a system, comprising: A) a memory configured tostore a template of a patterned document, which comprises at least onestatic element and at least one information field, wherein the templatecontains i) a text and coordinates for the at least one static elementof the patterned document and ii) coordinates for the at least oneinformation field of the patterned document; B) a processing device,coupled to the memory, the processing devise configured to: (a) receivea current image of a series of images of a copy of the patterneddocument; (b) perform optical symbol recognition (OCR) of the currentimage to produce an OCR text and corresponding coordinates for eachsymbol of the OCR text; (c) identify parameters of a coordinatetransformation to transform coordinates of the previous image intocoordinates of the current image; (d) associate at least part of the OCRtext with one or more clusters of symbol sequences, wherein the OCR textis produced by processing the current image and wherein the symbolsequences are produced by processing one or more previously receivedimages of the series of images; (e) identify parameters of a coordinatetransformation to transform the coordinates of the current image of theseries of images, into coordinates of the template; (f) identify in thecoordinate system of the template, one or more clusters of symbolsequences that corresponds to an information field of the at least oneinformation field; (g) produce, for each of the one or more clustersthat corresponds to the information field, a median string representingthe cluster of symbol sequences for the information field; and (h)produce, using the median string of each of the one or more clusters, aresulting OCR text that represents an original text of the informationfield of the copy of the patterned document.

Yet another embodiment is a computer-readable non-transitory storagemedium comprising executable instructions that, when executed by aprocessing device, cause the processing device to: (a) receive a currentimage of a series of images of a copy of a patterned document, whereinthe patterned document has at least one static element and at least oneinformation field; (b) perform optical character recognition (OCR) ofthe current image to produce an OCR text and corresponding coordinatesfor each symbol of the OCR text; (c) identify parameters of a coordinatetransformation to transform coordinates of the current image intocoordinates of a template, wherein the template contains i) a text andcoordinates for the at least one static element of the patterneddocument and ii) coordinates for the at least one information field ofthe patterned document; (d) identify in the OCR text for the currentimage in the coordinate system of the template a text fragment thatcorresponds to an information field of the at least one informationfield; (e) associate in the coordinate system of the template the textfragment that corresponds to the information field with one or moreclusters of symbol sequences, wherein the text fragment is produced byprocessing the current image and wherein the symbol sequences areproduced by processing one or more previously received images of theseries of images; (f) produce, for each cluster of the one or moreclusters, a median string representing the cluster of symbol sequencesfor the information field; and (g) produce, using the median string ofeach of the one or more clusters, a resulting OCR text that representsan original text of the information field of the copy of the patterneddocument.

And yet another embodiment is a computer-readable non-transitory storagemedium comprising executable instructions that, when executed by aprocessing device, cause the processing device to: (a) receive a currentimage of a series of images of a copy of a patterned document, whereinthe current image at least partially overlaps with a previous image ofthe series of images and the patterned document has at least one staticelement and at least one information field; (b) perform optical symbolrecognition (OCR) of the current image to produce an OCR text andcorresponding coordinates for each symbol of the OCR text; (c) identifyparameters of a coordinate transformation to transform coordinates ofthe previous image into coordinates of the current image; (d) associateat least part of the OCR text with one or more clusters of symbolsequences, wherein the OCR text is produced by processing the currentimage and wherein the symbol sequences are produced by processing one ormore previously received images of the series of images; (e) identifyparameters of a coordinate transformation to transform the coordinatesof the current image of the series of images, into coordinates of atemplate of the patterned document, wherein the template contains i) atext and coordinates for the at least one static element of thepatterned document and ii) coordinates for the at least one informationfield of the patterned document; (f) identify in the coordinate systemof the template, one or more clusters of symbol sequences thatcorresponds to an information field of the at least one informationfield; (g) produce, for each of the one or more clusters thatcorresponds to the information field, a median string representing thecluster of symbol sequences for the information field; and (h) produce,using the median string of each of the one or more clusters, a resultingOCR text that represents an original text of the information field ofthe copy of the patterned document.

FIGURES

FIG. 1 schematically illustrates matching of optical characterrecognition (OCR) results for each of images 102, 104, 106, and 108 of aseries of images of a patterned document with template 112 of thepatterned document.

FIG. 2 presents a block diagram of one illustrative example of a methodof automated data extraction from one or more information fields of apatterned document using a series of images of the document.

FIG. 3 schematically illustrates matching OCR results for pairs ofimages for a sequence of images of a patterned document and a subsequentmatching of combined OCR result for the sequence of images of thepatterned document with a template of the patterned document.

FIG. 4 presents a block diagram of another illustrative example of amethod of automated data extraction from one or more information fieldsof a patterned document using a series of images of the document.

FIG. 5 schematically depicts a California driver license as anillustrative example of a patterned document, which has one or morestatic elements and one or more information fields.

FIG. 6 schematically depicts a computer system, which may be used forimplementing disclosed methods.

DETAILED DESCRIPTION Related Documents

The following documents, which are incorporated herein by reference intheir entirety, may be helpful for understanding the present disclosure:a) U.S. patent application Ser. No. 15/168,548 filed May 31, 2016; andb) U.S. patent application Ser. No. 15/168,525 filed May 31, 2016.

DISCLOSURE

Described herein are methods and systems for performing opticalcharacter recognition (OCR) of a series of images depicting symbols of acertain writing system. The writing systems whose symbols may beprocessed by the systems and methods described herein include alphabetsthat have separate symbols, or glyphs, representing individual sounds,as well as hieroglyphic writing systems that have separate symbolsrepresenting larger units, such as syllables or words.

The disclosed methods may be particularly useful for optical characterrecognition from patterned documents. In some embodiments, the disclosedmethods allow an automatic entry of data from information fields ofphysical copies of documents, such as paper copies of documents, intoinformational systems and/or data bases using a computer system, whichis equipped with or connected to a camera. In certain embodiments, thecomputer system may be a mobile device, such as a tablet, a cellularphone or a personal digital assistant, which is equipped with a camera.In some embodiments, the automatic data entry may performed in an onlineregime. Most or all steps of the disclosed methods may be performeddirectly by the computer system, such as a mobile device.

Existing methods of data extraction from paper documents, such as ABBYYFlexiCapture, usually use only a single image of a document. The singleimage may contain one or more defects, such as a digital noise, poorfocusing or image clarity, glares, etc. which may be routinely caused bythe camera shake, inadequate illumination, incorrectly chosen shutterspeed or aperture, and/or other conditions and attenuatingcircumstances. As the result, data extracted from the single image ofthe document may contain optical character recognition errors. Discloseddata extraction methods utilize a series of images of a document.Although an individual image of the series may contain one or moredefects and therefore, corresponding OCR results (extracted data) forthe individual image may contain errors, combining OCR results(extracted data) for multiple images of the series may significantlyimprove quality of extracted data compared to data, which was extractedusing a single image of the document, due to reduction or elimination ofstatistical errors and filtering out of random events. Thus, discloseddata extraction methods may be extremely noise resistant so that randomerrors in recognizing individual character(s) in the document, which mayoccur for an individual image, do not affect a final result of opticalcharacter recognition and/or data extraction.

In some embodiments, from a user's point of view, a process of obtainingdata from a document may be as follows. A user may start an application,which is based on one of the disclosed methods, on a computer system,such as a mobile device, and activate a regime of the application, whichmay start a process of obtaining of series of images of a document by acamera, which is either an integral part of the computer system or isconnected to the computer system. In many embodiments of the disclosedmethods, the computer system, such as a mobile device, does not storethe obtained images in its memory. The activation of the regime mayinitiate obtaining and processing of images of the series by a processorof the computer system, such as a mobile device. In some embodiments,the user may select a portion of the document to be scanned bypositioning a crosshair of a viewfinder, which may be shown on a screenof the computer system, on that portion of the document. For example,the user may position a crosshair of a viewfinder, which is shown ascreen of a mobile device, on a desired portion of the document.Individual images of the series do not need to contain the wholedocument. Thus, in some embodiments, at least some individual images ofthe series may contain only portions of the document, while partiallyoverlapping with each other. In some embodiments, the camera may bescanned over the document. Preferably, such scanning covers allfragments or portions of the document. In many embodiments, the scanningmay be performed manually by the user. The processor performing one ofthe disclosed methods may identify relevant information field(s), fromwhich data/information/text should be obtained or extracted by theoptical character recognition, in each of the obtained images of theseries on an on-going basis. In an interface of the application, aresult of such identification may be shown as follows: the processorperforming one of the disclosed methods may communicate with a screen ordisplay of the computer system to highlight on the screen or display anindividual image's fragment, in which an information field has beenidentified, and to demonstrate to the user the obtained or extracteddata/information/text in order to confirm their accuracy. The processormay also on the screen or display provide instructions to the user, inwhich direction the camera should be scanned for obtaining furtherimages.

The term “patterned document” may refer to a document, which has thesame unchanged pattern or layout among individual documents, whichbelong to a particular type of a patterned document. Examples ofpatterned documents include, but no limited to fill-in forms, such asapplication forms, questionnaires, invoices, identification documents,such as passports and driver's licenses. A patterned document usuallyincludes one or more information fields. A quantity of informationfields, their design and positions do not change among individualdocuments, which belong to the same type of a patterned document.

In some embodiments, a patterned document may have at least one staticelement and at least one information field. The term “static element”refers to an element, which remains unchanged, both in its content andits position, among individual documents, which belong to a particulartype of a patterned document. The term “information field” refers to afield, which carries a distinct information for each individualdocument, which belongs to a particular type of a patterned document.FIG. 5 uses a California driver license as an example of a patterneddocument. In FIG. 5. elements 512, 514 and 516 are static as theypresent in all California's driver license having this particularpattern. On the other hand, elements 502, 504 and 506 representinformation fields as they carry the information, which is individualfor each particular driver's license. In some embodiments, aninformation field may have a corresponding static element, which may,for example, define a type of information presented in the informationfield. For example, in FIG. 5. each of information fields 502, 504 and506 has a corresponding static element (512, 514, 516, respectively). Insome embodiments, an information field may have no corresponding staticelement. In FIG. 5, a home address entry is an example of suchinformation field.

The disclosed methods may utilize a template of a patterned document.The use of a template may allow for an automatic determination whether aprocessed document corresponds to a known type of a patterned document,for which a template is stored in a memory of a computer system, such asa mobile device. The use of template may also allow for a determinationof a position of a current image (of the series of images) of apatterned document with respect to a template of the patterned documentin a situation when the current image shows only a portion or a fragmentof the patterned document.

A template of a patterned document may contain a) information, such as atextual content and a positional information, such as coordinates, forone or more static elements of the document and b) positionalinformation, such as coordinates, for one or more information fieldsdocument. Preferably, the template contains information regarding thetextual content and coordinates for each of the static elements of thedocument as well as coordinates for each of the information fields.Preferably, the template is not an image of a portion of the documentbut an image of the document as a whole.

A template of a patterned document may be obtained using a number ofways. For example, a template of a patterned document may be preparedfrom an electronic version of the patterned document. If an electronicversion of a patterned document is not available, then a template may beprepared using optical character recognition techniques from a physicalcopy, such as a paper copy, of the patterned document. The copy of thepatterned document for the template may be a blank copy of the patterneddocument, i.e. a copy of the patterned document with unfilled(containing no text/information) information field(s), or a filled copyof the patterned document, i.e. a copy of the patterned document withfilled (containing a text/information) information field(s). In someembodiments, obtaining a template of a patterned document from aphysical copy of the patterned document may involve: a) obtaining animage of the copy of the patterned document as a whole; b) performingoptical character recognition (OCR) of the obtained image to obtain OCRresults, which include an OCR text and its corresponding layoutinformation, including coordinates for each symbol and group of symbolsin the OCR text; c) determining from the OCR results coordinates for oneor more information fields of the patterned documents. The determiningin c) may involve identifying the one or more information fields in theOCR results. In some embodiments, such identifying may be performedmanually. The identified field may be assigned a correspondingidentification. For example, in FIG. 5, fields 502, 504 and 506 may beassigned identifications “driver license number,” “last name” and “firstname”, respectively. For each new type of a patterned document, atemplate may be obtained from a first image of a copy of the pattereddocument processed by a computer system

Method One

In one embodiment, an OCR method may involve a) comparing each image ofa series of images of a copy of a patterned document with a template ofthe patterned document to identify a data stack (a cluster of symbolsequences), which corresponds to a particular information field of thepatterned document; and b) identifying the best (the most accurate) OCRresult of the series of images using a median string and a medianpermutation for clusters of symbol sequences, which were collected fordifferent images of the series, for the particular information field ofthe patterned document.

FIG. 1 schematically matching or comparing of each image 102, 104, 106and 108 of an image series for a copy of a patterned document withtemplate 112 of the patterned document. For each image of the series,the optical character recognition (OCR) may provide an OCR text and itscorresponding layout, including coordinates for each symbol of the OCRtext. In FIG. 1 element 132 illustrates an OCR text for image 102. Thetemplate may include the following information: a text, whichcorresponds to one or more static elements, such as a title of thedocument, titles for corresponding information fields, examples offilling in information fields, etc., coordinates for each symbol of theone or more static elements and coordinates for one or more informationfields. In FIG. 1 on template 112, elements 142 illustrate staticelements, while element 144 illustrates an information field. Matchingor comparing an image of the series with the template may involve aprojective transformation for each image of the series/template pair.FIG. 1 shows such projective transformations as elements 122, 124, 126and 128.

FIG. 2 presents a flow chart of one illustrative example of a method foroptical character recognition for a series of images of a patterneddocument. The method and/or each of its individual functions, routines,subroutines, or operations may be performed by one or more processors ofa computer system executing the method. In some embodiments, method 200may be performed by a single processing thread. Alternatively, method200 may be performed by two or more processing threads, each threadexecuting one or more individual functions, routines, subroutines, oroperations of the method. In an illustrative example, the processingthreads implementing method 200 may be synchronized (e.g., usingsemaphores, critical sections, and/or other thread synchronizationmechanisms). Alternatively, the processing threads implementing method200 may be executed asynchronously with respect to each other.Therefore, while FIG. 2 and the associated description lists theoperations of method 200 in certain order, various implementations ofthe method may perform at least some of the described operations inparallel and/or in arbitrary selected orders.

For clarity and conciseness, the present description assumes that theprocessing of each image of a patterned document is initiated after theimage is received by the computer system implementing the method, andthat such processing is substantially completed before the next image isreceived. However, in various alternative implementations, processing ofconsecutive images may overlap in time (e.g., may be performed bydifferent threads or processes that are running on one or moreprocessors). Alternatively, two or more images may be buffered andprocessed asynchronously with respect to receiving the other images of aplurality of images received by the computer system implementing themethod.

In some embodiments, two consecutive images of the series may benon-overlapping.

Yet in some embodiments, two consecutive images of the series may be atleast partially overlapping.

In some embodiments, images of the series may depict at least partiallyoverlapping document fragments, and may differ by the image scale,shooting angle, shutter speed, aperture, image brightness, glaring,presence of external objects that at least partially cover the originaltext, and/or other image features, visual artifacts, and imaging processparameters.

The image called “the current image” further in the text is referred as“i-th image” in FIG. 2.

At block 210, the computer system implementing the method may receivethe current image (of the series of images) of a copy of a patterneddocument. The current image may be acquired for example, by a camera. Insome embodiments, the camera may be an integral part of the computersystem Yet in some embodiments, the camera may be a separate device,which is configured to transfer images to the computer system

At block 215, the computer system may perform an optical characterrecognition of the current image, thereby producing OCR results, i.e. arecognized text (OCR text) and corresponding layout information, whichmay include coordinates for each symbol of the recognized text. Thelayout information may associate one or more symbols of the recognizedtext and/or one or more groups of symbols of the recognized text.

At block 220, the computer system may identify one or more matchingtextual artifacts, such as one or more symbols and one or more groups ofsymbols, in the OCR results for the current image and a template of thepatterned document, which may be stored in a memory of the computersystem. A textual artifact may be represented by a sequence of symbols(e.g., words) having a low frequency of occurrence within the OCRproduced text (e.g., the frequency that does not exceed a specifiedfrequency threshold, which may be set to 1 to reference a uniquesequence of symbols). In an illustrative example, a low frequency wordmay be identified by sorting the OCR-produced words in the order oftheir respective frequencies and selecting a word having the lowestfrequency. In certain implementations, only sequences of symbols, therespective lengths of which exceed a certain threshold length may beutilized by the method, since shorter sequences of symbols produce lessreliable base points.

At block 225, the computer system may employ the layout informationassociated with the identified textual artifacts to identify at leastone base point representing each textual artifact within each image ofthe series/template pair. In an illustrative example, a base pointassociated with the identified sequence of symbols may be represented bythe center of the minimum bounding rectangle of the sequence of symbols.In another illustrative example, two or more base points associated withthe identified sequence of symbols may be represented by the comers ofthe minimum bounding rectangle of the sequence of symbols.

At block 230, the computer system may inspect the identified base pointsand discard at least some of them in view of the chosen filteringcriteria. Filtering of base points is disclosed, for example, in U.S.patent application Ser. Nos. 15/168,548 and 15/168,525, including FIGS.2A-2B, 3 and 4 as well as the corresponding text, which is incorporatedherein by reference in its entirety.

At block 235, the computer system may construct the coordinatetransformation converting coordinates of the current image intocoordinates of the template. The present method assumes that, at leastfor the chosen pairs of each image of the series/template, coordinatesof an arbitrary chosen point in the first image may be produced byapplying a projective transformation to the coordinates of the samepoint in the second Image.

“Projective transformation” herein refers to a transformation that mapslines to lines, but does not necessarily preserve parallelism. Aprojective transformation can be expressed by the following equations:

$\begin{matrix}{X = \frac{{{Ax}_{1}*x} + {{Ax}_{2}*y} + {Ax}_{3}}{{{Ax}_{4}*x} + {{Ay}_{4}*y} + 1}} & (1) \\{Y = \frac{{{Ay}_{1}*x} + {{Ay}_{2}*y} + {Ay}_{3}}{{{Ax}_{4}*x} + {{Ay}_{4}*y} + 1}} & (2)\end{matrix}$

wherein (x,y) and (X,Y) represent coordinates of an arbitrary chosenpoint in the first image (current image) and the second image(template), respectively. The transformation coefficients Ax₁, Ax₂, Ax₃,Ax₄, Ay₁, Ay₂, Ay₃, and Ay₄ may be determined based on known coordinatesof at least four base points in each of the two images, which wouldproduce a system of eight equations with eight variables. Once thetransformation coefficients have been determined, the equations (1) and(2) may be applied to coordinates of an arbitrary chosen point in thefirst image in order to determine coordinates of the same point in thesecond image.

In certain implementations, more than four pair of base points may beidentified for a given pair of each image of series/template byoperations referenced by blocks 225-230, in which case theover-determined system may be resolved by regression analysis methods,such as the method of least squares. Coordinates transformations betweenimages using base points are disclosed, example, in U.S. patentapplication Ser. Nos. 15/168,548 and 15/168,525, including FIG. 5A aswell as the corresponding text, which is incorporated herein byreference in its entirety.

At block 240, the computer system may overlay the template over thecurrent image (converted into the coordinates of the template) toidentify one or more textual fragments, which correspond to one or moreinformation fields of the patterned document. This way, the computersystem may discard a static portion (i.e. a portion, which correspondsto one or more static elements of the patterned document) of the currentimage, while forming from the text fragment's portion, which correspondsto one or more information fields of the current image, a data stack,which may include one or more symbol sequences, such as a word.

At block 245, the computer may associate one or more symbol sequences,such as a word, within the textual fragment's portion, which correspondsto one or more information fields of the current image, with a clusterof matching symbol sequences produced by OCR of the previously processedimages. Association of a symbol sequence with a cluster of matchingsymbol sequences is disclosed, for example, in U.S. patent applicationSer. Nos. 15/168,548 and 15/168,525, see e.g. FIGS. 5A-5B, 6 as well asthe corresponding text, which is incorporated herein by reference in itsentirety. Preferably, the computer system associates each symbolsequence, such as a word, within the textual fragment's portion, whichcorresponds to one or more information fields of the current image, witha cluster of matching symbol sequences produced by OCR of the previouslyprocessed images.

At block 250, the computer system may increment a counter for thecurrent image in the series of images. Operations of block 250 arepresented in FIG. 2 for readability of the associated description, andmay be omitted in various implementations of the method.

At block 255, the computer system may determine whether there is a nextimage in the series of images. If so, the method may loop back to block210.

At block 260, the computer system may identify a median string for acluster of matching symbol sequences, which corresponds to aninformation field of the processed copy of the patterned document, suchthat the identified median string would represent the OCR results forthe series of images of the processed copy of the patterned document fora fragment of the document, which corresponds to the information field.Identification of median strings for clusters of matching symbolsequences is disclosed, for example, in U.S. patent application Ser.Nos. 15/168,548 and 15/168,525, see e.g. FIGS. 7A-C as well as thecorresponding text, which is incorporated herein by reference in itsentirety. Preferably, the computer system identifies a median string foreach cluster of matching symbol sequences, which correspond to aparticular information field of the processed copy of the patterneddocument, while doing so for each information field of the patterneddocument.

If a particular information field has several, i.e. more than one,symbol sequences, such as words, (for example, a home addressinformation field may comprise several words) and then at block 265, thecomputer system may identify an order, in which the symbol sequencesrepresenting the above-mentioned clusters (the median strings ofclusters) should appear in the resulting text, which corresponds to theinformation field. For example, the images representing the originaldocument may depict at least partially overlapping document fragments,and may differ by the image scale, shooting angle, shutter speed,aperture, image brightness, glaring, presence of external objects thatat least partially cover the original text, and/or other image features,visual artifacts, and imaging process parameters. Therefore, the textsproduced by the OCR of each individual image may differ by one or morewords being present or absent in each OCR result, by variations in thesymbol sequences representing the words of the original text, and/or bythe order of the symbol sequences.

In certain implementations, the computer system may compare a pluralityof permutations of the symbol sequences that represent the identifiedclusters. The median permutation may be identified as the permutationhaving the minimal sum of Kendall tau distances to all otherpermutations. The Kendall tau distance between a first permutation and asecond permutation may be equal to the minimum number of swappingoperations required by the bubble sort algorithm to transform the firstpermutation into the second permutation. Identification of an order forclusters is disclosed, for example, in U.S. patent application Ser. Nos.15/168,548 and 15/168,525, which is incorporated herein by reference inits entirety.

In various implementations, the operations described by blocks 260 and265 may be performed in the reverse sequence or in parallel.Alternatively, certain implementations may omit certain operationsdescribed by blocks 260 and/or 265.

At block 270, the computer system may produce a resulting OCR text,which corresponds to a particular information field of the patterneddocument. Preferably, the computer system produces a resulting OCR textfor each information field of the patterned document. If a particularinformation field has only one symbol sequence, such a word, then thecomputer system may produce the resulting OCR text for such fielddirectly from the median string identified in block 260. If a particularinformation field has several, i.e. more than one, symbol sequences,such as words, then the computer system may produce the resulting OCRtext for such field after identifying an order for the correspondingsymbol sequences at block 265.

Method Two

In another embodiment, an OCR method may involve a) comparing ormatching OCR results for a series of images of a copy of a patterneddocument to obtain an aligned stack of OCR results for the series; andb) comparing or matching the aligned stack of the OCR results for theseries in coordinates of the last image of the series with a template ofthe patterned document (a template overlaying) and identifying the best(the most accurate) OCR result of the series of images using a medianstring and a median permutation for each information field of thepatterned document.

FIG. 3 schematically illustrates series of images 302, 304 and 306 of acopy of a patterned document. OCR results of image 304 iscompared/matched (322) with OCR results of image 302, while OCR resultsof image 306 is compared/matched (324) with OCR results of image 304. Asthe result of such comparing/matching, aligned stack 312 is obtained.Aligned stack 312 is compared/matched (342) with template 332 of thepatterned document to identify the best (the most accurate) OCR resultof the series of images using a median string a median permutation forinformation field B (334) of the patterned document.

FIG. 4 presents a flow chart of another illustrative example of a methodfor optical character recognition for a series of images of a patterneddocument. The method and/or each of its individual functions, routines,subroutines, or operations may be performed by one or more processors ofa computer system executing the method. In some embodiments, method 400may be performed by a single processing thread. Alternatively, method400 may be performed by two or more processing threads, each threadexecuting one or more individual functions, routines, subroutines, oroperations of the method. In an illustrative example, the processingthreads implementing method 400 may be synchronized (e.g., usingsemaphores, critical sections, and/or other thread synchronizationmechanisms). Alternatively, the processing threads implementing method400 may be executed asynchronously with respect to each other.Therefore, while FIG. 4 and the associated description lists theoperations of method 400 in certain order, various implementations ofthe method may perform at least some of the described operations inparallel and/or in arbitrary selected orders.

For clarity and conciseness, the present description assumes that theprocessing of each image of a patterned document is initiated after theimage is received by the computer system implementing the method, andthat such processing is substantially completed before the next image isreceived. However, in various alternative implementations, processing ofconsecutive images may overlap in time (e.g., may be performed bydifferent threads or processes that are running on one or moreprocessors). Alternatively, two or more images may be buffered andprocessed asynchronously with respect to receiving the other images of aplurality of images received by the computer system implementing themethod.

In some embodiments, images of the series may depict at least partiallyoverlapping document fragments, and may differ by the image scale,shooting angle, shutter speed, aperture, image brightness, glaring,presence of external objects that at least partially cover the originaltext, and/or other image features, visual artifacts, and imaging processparameters. The two images are individually referenced herein as “thecurrent image” (also referred to as “i-th image” in FIG. 4) and “theprevious image” (also referred to as “(i-1)-th image” in FIG. 4).

Overall, steps 410-450 are substantially identical to steps 110-150disclosed in U.S. patent application Ser. Nos. 15/168,548 and15/168,525, see e.g. FIG. 1 as well as the corresponding text, which isincorporated herein by reference in its entirety.

At block 410, the computer system implementing the method may receive acurrent image of series of images for a copy of a patterned document.The patterned document may include one or more static elements and oneor more information fields. The current image may be acquired forexample, by a camera. In some embodiments, the camera may be an integralpart of the computer system. Yet in some embodiments, the camera may bea separate device, which is configured to transfer acquired images tothe computer system

At block 415, the computer system may perform an optical characterrecognition of the current image, thereby producing OCR results, i.e. arecognized text (OCR text) and corresponding layout information, whichmay include coordinates for each symbol of the recognized text.

At block 420, the computer system may identify matching textualartifacts in the OCR results representing the pair of images, which maybe, for example, the current image and the previous image. A textualartifact may be represented by a sequence of symbols (e.g., words)having a low frequency of occurrence within the OCR produced text (e.g.,the frequency that does not exceed a specified frequency threshold,which may be set to 1 to reference a unique sequence of symbols). In anillustrative example, a low frequency word may be identified by sortingthe OCR-produced words in the order of their respective frequencies andselecting a word having the lowest frequency. In certainimplementations, only sequences of symbols, the respective lengths ofwhich exceed a certain threshold length may be utilized by the method,since shorter sequences of symbols produce less reliable base points.

At block 425, the computer system may employ the layout information,such as coordinates, associated with the identified textual artifacts toidentify at least one base point representing each textual artifactwithin each image of the pair of images. In an illustrative example, abase point associated with the identified sequence of symbols may berepresented by the center of the minimum bounding rectangle of thesequence of symbols. In another illustrative example, two or more basepoints associated with the identified sequence of symbols may berepresented by the comers of the minimum bounding rectangle of thesequence of symbols.

At block 430, the computer system may inspect the identified base pointsand discard at least some of them in view of the chosen filteringcriteria. In an illustrative example, the computer system may verifythat arbitrarily selected groups of the matching base points exhibitcertain geometric features that are invariant with respect to the chosenimages. Filtering of base points is disclosed, for example, in U.S.patent application Ser. Nos. 15/168,548 and 15/168,525, see e.g. FIGS.2A-2B, 3 and 4 as well as the corresponding text, which is incorporatedherein by reference in its entirety.

At block 435, the computer system may construct a coordinatetransformation converting coordinates of one image of the pair of imagesinto coordinates of another image of the pair of images. The presentmethod assumes that, at least for the chosen pairs of images,coordinates of an arbitrary chosen point in the first image may beproduced by applying a projective transformation to the coordinates ofthe same point in the second image.

Preferably, the computer system constructs a coordinate transformationconverting coordinates of the previous image into coordinates of thecurrent image.

“Projective transformation” herein refers to a transformation that mapslines to lines, but does not necessarily preserve parallelism. Aprojective transformation can be expressed by the following equations:

$\begin{matrix}{X = \frac{{{Ax}_{1}*x} + {{Ax}_{2}*y} + {Ax}_{3}}{{{Ax}_{4}*x} + {{Ay}_{4}*y} + 1}} & (1) \\{Y = \frac{{{Ay}_{1}*x} + {{Ay}_{2}*y} + {Ay}_{3}}{{{Ax}_{4}*x} + {{Ay}_{4}*y} + 1}} & (2)\end{matrix}$

wherein (x,y) and (X,Y) represent coordinates of an arbitrary chosenpoint in the first image and the second image, respectively. Thetransformation coefficients Ax₁, Ax₂, Ax₃, Ax₄, Ay₁, Ay₂, Ay₃, and Ay₄may be determined based on known coordinates of at least four basepoints in each of the two images, which would produce a system of eightequations with eight variables. Once the transformation coefficientshave been determined, the equations (1) and (2) may be applied tocoordinates of an arbitrary chosen point in the first image in order todetermine coordinates of the same point in the second image.

In certain implementations, more than four pair of base points may beidentified for a given pair of images by operations referenced by blocks425-430, in which case the over-determined system may be resolved byregression analysis methods, such as the method of least squares.

Projective transformations are disclosed, for example, in U.S. patentapplication Ser. Nos. 15/168,548 and 15/168,525, including Figure SA aswell as the corresponding text, which is incorporated herein byreference in its entirety.

At block 440, the computer system may associate one or more symbolsequences produced by OCR of the current image with one or more clustersof matching symbol sequences produced by OCR of the previously processedimages. The computer system may employ the above-referenced coordinatetransformations to compare positions of recognized symbol sequences inthe current and previous images, and thus identify groups of symbolsequences that are likely to represent the same fragment of the originaldocument. Association of a symbol sequence with a cluster of matchingsymbol sequences is disclosed, for example, in U.S. patent applicationSer. Nos. 15/168,548 and 15/168,525, including FIGS. 5A-5B, 6 as well asthe corresponding text, which is incorporated herein by reference in itsentirety.

At block 445, the computer system may increment the counter for thecurrent image in the series of images. Notably, operations of block 445are presented in FIG. 4 for readability of the associated description,and may be omitted in various implementations of the method.

At block 450, the computer system may determine whether there is a nextimage; if so, the method may loop back to block 410.

At block 455, the computer system may construct a coordinatetransformation converting coordinates of the last image in the series ofimages into coordinates of a template of the patterned document. Suchtransformation converts one or more clusters of matching symbolsequences, which were formed by processing all the images of the series,into the coordinates of the template.

The coordinate transformation at block 455 may performed similarly tothe coordinate transformation at block 435.

At block 460, the computer system may identify a cluster of symbolsequences which corresponds to a particular information field in theprocessed copy of the patterned document from the one or more clustersof matching symbol sequences by comparing the one or more clusters inthe coordinates of the template with the template itself.

At block 465, the computer system may identify a median string for acluster of symbol sequences, which corresponds to a particularinformation field of the processed copy of the patterned document, suchthat the identified median string would represent the OCR result for afragment of the current image, which corresponds to the informationfield. Identification of median strings for clusters of symbol sequencesis disclosed, for example, in U.S. patent application Ser. Nos.15/168,548 and 15/168,525, see e.g. FIGS. 7A-Cas well as thecorresponding text, which is incorporated herein by reference in itsentirety. Preferably, the computer system identifies a median string foreach cluster of matching symbol sequences, which correspond to aparticular information field of the processed copy of the patterneddocument, while doing so for each information field of the patterneddocument.

If a particular information field has several, i.e. more than one,symbol sequences, such as words, (for example, a home addressinformation filed may comprise several, i.e. more than one words) thenat block 470, the computer system may identify an order, in which thesymbol sequences representing the above-mentioned clusters (the medianstrings of clusters) should appear in the resulting text, whichcorresponds to the information field. For example, the imagesrepresenting the original document may depict at least partiallyoverlapping document fragments, and may differ by the image scale,shooting angle, shutter speed, aperture, image brightness, glaring,presence of external objects that at least partially cover the originaltext, and/or other image features, visual artifacts, and imaging processparameters. Therefore, the texts produced by the OCR of each individualimage may differ by one or more words being present or absent in eachOCR result, by variations in the symbol sequences representing the wordsof the original text, and/or by the order of the symbol sequences.

In certain implementations, the computer system may compare a pluralityof permutations of the symbol sequences that represent the identifiedclusters. The median permutation may be identified as the permutationhaving the minimal sum of Kendall tau distances to all otherpermutations. The Kendall tau distance between a first permutation and asecond permutation may be equal to the minimum number of swappingoperations required by the bubble sort algorithm to transform the firstpermutation into the second symbol permutation. Identification of anorder for clusters is disclosed, for example, in U.S. patent applicationSer. Nos. 15/168,548 and 15/168,525, see e.g. FIG. 5B as well as thecorresponding text, which is incorporated herein by reference in itsentirety.

In various implementations, the operations described by blocks 465 and470 may be performed in the reverse sequence or in parallel.Alternatively, certain implementations may omit certain operationsdescribed by blocks 465 and/or 470.

At block 475, the computer system may produce a resulting OCR text,which corresponds to a particular information field of the processedcopy of the patterned document. Preferably, the computer system producesa resulting OCR text for each information field for the processed copyof the patterned document. If a particular information field has onlyone symbol sequence, such a word, then the computer system may producethe resulting OCR text for such field directly from the median stringidentified in block 465. If a particular information field has several,i.e. more than one, symbol sequences, such as words, then the computersystem may produce the resulting OCR text for such field afteridentifying an order for the corresponding symbol sequences at block470.

At least one advantage of the method, which is illustrated in FIG. 2, isthat it allows for a better identification of objects due to a lowererror in coordinate transformation calculation. At least one advantageof the second method, which is illustrated in FIG. 4, is its higherreliability. Individual images contain more text than a template. Thus,a successful recognition of some static elements in at least one imageof the series may guarantee a successful overlaying with the templateeven if the same static elements are not well recognized in many ofother images of the series.

The computer system may be, for example, a computer system, which isillustrated in FIG. 6. The computer system may be a device capable ofexecuting a set of instructions (sequential or otherwise) that specifyoperations to be performed by that computer system For example, apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), or a cellular telephone.

FIG. 6 depicts a component diagram of an example computer system 600within which a set of instructions, for causing the computer system toperform any one or more of the methods discussed herein, may beexecuted. The computer system 600 may be connected to other computersystem in a LAN, an intranet, an extranet, or the Internet. The computersystem 600 may operate in the capacity of a server or a client computersystem in client-server network environment, or as a peer computersystem in a peer-to-peer (or distributed) network environment. Thecomputer system 600 may be a provided by a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, or any computer system capable of executing a set ofinstructions (sequential or otherwise) that specify operations to beperformed by that computer system Further, while only a single computersystem is illustrated, the term “computer system” shall also be taken toinclude any collection of computer systems that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

Exemplary computer system 600 includes processor 602, main memory 604(e.g., read-only memory (ROM) or dynamic random access memory (DRAM)),and data storage device 618, which communicate with each other via bus630.

Processor 602 may be represented by one or more general-purposeprocessing devices such as a microprocessor, central processing unit, orthe like. More particularly, processor 602 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. Processor 602 may alsobe one or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), network processor, or thelike. Processor 602 is configured to execute instructions 626 forperforming the operations and functions of one of the methods of FIG. 2or FIG. 4.

Computer system 600 may further include network interface device 622,video display unit 610, a character input device 612 (e.g., a keyboard),and touch screen input device 614.

Data storage device 618 may include a computer-readable storage medium624 on which is stored one or more sets of instructions 626 embodyingany one or more of the methods or functions described herein.Instructions 626 may also reside, completely or at least partially,within main memory 604 and/or within processor 602 during executionthereof by computer system 600, main memory 604 and processor 602 alsoconstituting computer-readable storage media. Instructions 626 mayfurther be transmitted or received over network 616 via networkinterface device 622.

In certain implementations, instructions 626 may include instructionsfor performing one or more functions of methods illustrated in FIGS. 2and 4. While computer-readable storage medium 624 is shown in theexample of FIG. 6 to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and softwarecomponents, or only in software.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “determining”, “computing”, “calculating”, “obtaining”,“identifying,” “modifying” or the like, refer to the actions andprocesses of a computer system, or similar electronic computer system,that manipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions. It is to be understood that the abovedescription is intended to be illustrative, and not restrictive.

Various other implementations will be apparent to those of skill in theart upon reading and understanding the above description. The scope ofthe disclosure should, therefore, be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled.

What is claimed is:
 1. A method, comprising: identifying, by aprocessing device, in each of a current image and a previous image of aseries of images of an original document wherein the current image atleast partially overlaps with the previous image, a correspondingplurality of base points, wherein each base point is associated with atleast one textural artifact of a plurality of textual artifacts in eachof the current image and the previous image using an OCR text of thecurrent image; identifying, using coordinates of matching base points inthe current image and the previous image, parameters of a coordinatetransformation converting coordinates of the previous image intocoordinates of the current image; associating, using the coordinatetransformation, at least part of the OCR text with a cluster of aplurality of clusters of symbol sequences, wherein the symbol sequencesare produced by processing one or more previously received images of theseries of images; identifying, for each cluster, a median stringrepresenting the cluster of symbol sequences; and producing, using themedian string, a resulting OCR text representing at least a portion ofthe original document.
 2. The method of claim 1, wherein identifying abase point further comprises determining a center of a minimum boundingrectangle of an associated textual artifact.
 3. The method of claim 1,wherein the coordinate transformation is provided by a projectivetransformation.
 4. The method of claim 1, wherein the median string hasa minimal sum of values of a pre-defined metric with respect to thecluster of symbol sequences.
 5. The method of claim 1, wherein producingthe median string comprises applying weight coefficients to each symbolsequence of the cluster of symbol sequences.
 6. The method of claim 1,wherein identifying the plurality of clusters of symbol sequencesfurther comprises: producing a graph comprising a plurality of nodes,wherein each node represents a symbol sequence, the graph furthercomprising a plurality of edges, wherein an edge connects a first symbolsequence produced by OCR of at least a part of a first image of theseries of images and a second symbol sequence produced by OCR of acorresponding part of a second image of the series of images.
 7. Amethod, comprising: identifying, by a processing device, in each of acurrent image and a previous image of a series of images of an originaldocument wherein the current image at least partially overlaps with theprevious image, a corresponding plurality of base points, wherein eachbase point is associated with at least one textual artifact of aplurality of textual artifacts in each of the current image and theprevious image using an optical character recognition (OCR) of thecurrent image producing an OCR text; identifying, using coordinates ofmatching base points in the current image and the previous image,parameters of a coordinate transformation converting coordinates of theprevious image into coordinates of the current image; associating, usingthe coordinate transformation, at least part of the OCR text with acluster of a plurality of clusters of symbol sequences, wherein thesymbol sequences are produced by processing one or more previouslyreceived images of the series of images; identifying an order ofclusters of symbol sequences, the order reflecting a layout of theoriginal document; and producing, in view of the order of clusters, aresulting OCR text representing at least a portion of the originaldocument.
 8. The method of claim 7, wherein the current image and theprevious image differ in at least one of: image scale, a shooting angle,image brightness, or presence of an external object that is covering atleast part of the original document.
 9. The method of claim 7, whereinthe coordinate transformation is provided by a projectivetransformation.
 10. The method of claim 7, wherein identifying the orderof clusters further comprises: producing a graph comprising a pluralityof nodes, wherein each node represents a symbol sequence, the graphfurther comprising a plurality of edges, wherein an edge connects afirst symbol sequence produced by OCR of at least a part of a firstimage of the series of images and a second symbol sequence produced byOCR of a corresponding part of a second image of the series of images.11. A method, comprising: identifying, by a processing device,parameters of a coordinate transformation to transform coordinates of acurrent image of a series of images of a copy of a patterned documentinto coordinates of a template, wherein the template contains i) a textand coordinates for at least one static element of the patterneddocument and ii) coordinates for at least one information field of thepatterned document; identifying in an optical character recognition(OCR) text for the current image in the coordinates of the template atext fragment that corresponds to an information field of the at leastone information field; associating in the coordinates of the templatethe text fragment that corresponds to the information field with one ormore clusters of symbol sequences, wherein the symbol sequences areproduced by processing one or more previously received images of theseries of images; producing, for each cluster of the one or moreclusters, a median string representing the cluster of symbol sequencesfor the information field; and producing, using the median string ofeach of the one or more clusters, a resulting OCR text that representsan original text of the information field of the copy of the patterneddocument.
 12. The method of claim 11, wherein the associating with theone or more clusters of symbol sequences further comprises identifyingan order of plural clusters.
 13. The method of claim 12, furthercomprising filtering the identified base points using invariantgeometric features of base point groupings.
 14. The method of claim 11,further comprising: identifying, using the OCR text, a plurality oftextual artifacts in each of the current image and the template, whereineach textual artifact is represented by a sequence of symbols that has afrequency of occurrence within the OCR text falling below a thresholdfrequency; identifying, in each of the current image and the template, acorresponding plurality of base points, wherein each base point isassociated with at least one textural artifact of the plurality oftextual artifacts; and wherein said identifying the parameters of thecoordinate transformation uses coordinates of matching base points inthe current image and the template.
 15. A method, comprising:identifying, by a processing device, parameters of a coordinatetransformation to transform coordinates of a previous image of a seriesof images of a copy of a patterned document into coordinates of acurrent image of the series of images, wherein the current image atleast partially overlaps with the previous image; associating at leastpart of an optical character recognition (OCR) text of the current imagewith one or more clusters of symbol sequences, wherein the symbolsequences are produced by processing one or more previously receivedimages of the series of images; identifying parameters of a coordinatetransformation to transform the coordinates of the current image of theseries of images, into coordinates of a template of the patterneddocument, wherein the template contains i) a text and coordinates forthe at least one static element of the patterned document and ii)coordinates for the at least one information field of the patterneddocument; identifying in the coordinates of the template, one or moreclusters of symbol sequences that corresponds to an information field ofthe at least one information field; producing, for each of the one ormore clusters that corresponds to the information field, a median stringrepresenting the cluster of symbol sequences for the information field;and producing, using the median string of each of the one or moreclusters, a resulting OCR text that represents an original text of theinformation field of the copy of the patterned document.
 16. The methodof claim 15, further comprising: identifying, using the OCR textproduced in b), a plurality of textual artifacts in each of the currentimage and the previous image, wherein each textual artifact isrepresented by a sequence of symbols that has a frequency of occurrencewithin the OCR text falling below a threshold frequency; identifying, ineach of the current image and the previous image, a correspondingplurality of base points, wherein each base point is associated with atleast one textural artifact of the plurality of textual artifacts; andwherein said identifying the parameters of the coordinate transformationuses coordinates of matching base points in the current image and theprevious image.
 17. The method of claim 16, further comprising filteringthe identified base points using invariant geometric features of basepoint groupings.
 18. The method of claim 15, wherein the associatingwith the one or more clusters of symbol sequences further comprisesidentifying an order of plural clusters.
 19. The method of claim 18,wherein the identifying the order of the plural clusters comprisesidentifying a median of permutations of the clusters.
 20. The method ofclaim 19, wherein the median of the permutations of the clusters has aminimal sum of Kendall tau distances to all other permutations.